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Enhancing Trust in Distributed Sensing for Dynamic Spectrum Access Networks

This paper discusses attack-tolerant, distributed sensing in dynamic spectrum access networks, addressing the underutilization of spectrum resources. The authors examine the necessity of secure cooperative sensing to counteract malfunctioning or compromised sensors. By employing cluster-based cooperative sensing and weighted gain combining, the proposed approach enhances the accuracy of sensor reports while safeguarding against type-1 and type-2 attacks. The methodology includes sensor selection and data fusion to improve detection performance, ensuring reliable wireless broadband access in rural areas by optimizing spectrum usage.

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Enhancing Trust in Distributed Sensing for Dynamic Spectrum Access Networks

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  1. Attack-Tolerant Distributed Sensing forDynamic Spectrum Access Networks Alexander W. Min, Kang G. Shin, and XinHu Real-Time Computing Laboratory (RTCL) The University of Michigan

  2. Spectrum is scarce resource Source: Federal Communications Commission (FCC)

  3. But, severely under-utilized 5.2 % Source: Shared Spectrum Company

  4. A new paradigm – Dynamic Spectrum Access spectrum sensing - key enabling technology

  5. Need for cooperative sensing + + 2 sec. 10 % -20 dB Single sensor with one-time sensing is NOT enough

  6. BUT, can we really trust sensors? Sensors can be mal-functioning or compromised

  7. This paper How to design secure cooperative sensing?

  8. System model • IEEE 802.22 WRAN • • 1st CR-based international standard • • Goal : provide wireless broadband access • in rural areas by reusing TV bands • Signal propagation model • • Received signal strength (RSS) • log-normal shadowing • Spectrum sensing model • • Energy detection (ED) • -- Simple,BUT inaccurate • -- ED’s output: Estimation of RSSs

  9. Spatially-correlated shadow fading • Construction of shadowing field Accurate modeling of realistic shadowing environments

  10. Attack model • Attack scenarios • • A sensor is mal-functioning • Distorted sensor reports • •A sensor is compromised • Attack types • • TYPE1: increase false-alarm rate by increasing RSS • • TYPE2: increase miss-detection rate by decreasing RSS • Final sensor reports • • Sensor report = Energy detector’s output + distortion GOAL: Detection of any abnormal sensor reports

  11. Cooperative sensing in 802.22 A D B C > > < 10 (threshold) Report(A) + Report(B) + Report(C) + Report(D) = 11 5 12 decision statistic PU activity : ON Type-1 Type-2 OFF Decision : 0 1 1

  12. Key features: Attack-Tolerant Distributed Sensing • Exploits shadow fading correlation in RSSs • Proposes cluster-based cooperative sensing • Safeguards both type-1 and type-2 of attacks • Employs weighted gain combining (WGC) Shadow fading correlation can be exploited to filter out abnormal sensing reports

  13. Our approach: (1) sensor selection • Q1)How to select sensors? • sensor diversity • correlation profile • Independent sensors • Correlatedsensors • [Visotskyet al. 05] • [Ghasemiet al. 07] • [Selenet al. 08] • “A double-edged sword”

  14. Our approach: (2) data fusion • Q2) How to make a final decision? Our focus

  15. Cross validation using correlation filter • Cooperative detection A F • • 0 : normal, 1: abnormal B E D … C sensor cluster Discard the sensor report if # of flags > threshold

  16. How to raise a flag? • Correlation filter design • • Compute conditional p.d.f. of sensor reports • -- Prob(sensor A’s report | sensor B’s report ) = ? • -- Shifted log-normal distribution • Key observation • Corr. shadow fading ≈ Corr. sensor reports abnormal abnormal • • Tradeoff in thresholds: over(under) filtering Correlation filter is efficient, but NOT perfect

  17. A new data-fusion rule • Weighted Gain Combining (WGC) • • Give different weights to sensor reports A B C D abnormal abnormal • • weight(A) > weight(B) > weight(C) > weight(D) = 0 • • Σweight(•) = 1 WGC further improves attack-tolerance

  18. Performance Evaluation • Simulation Setup • • 30 sensors in 5 clusters • • 10 compromised sensors • • Shadow fading σdB=4.5 dB • Testing schemes • 1) Equal gain combining (EGC) • 2) Statistic-based method (Outlier) [Kaligineediet al. 08] • – Reject any sensor reports outside range R • 3) Correlation Filter + EGC/WGC (ADSP)

  19. Impact of sensor clustering Clustering achieves 90 % detection performance Small performance degradation even under -23 dB

  20. ADSP successfully tolerates type-1 attacks EGC Outlier Filter + EGC High false-alarm Filter + WGC Low false-alarm Sensing scheduling can further improve attack-tolerance

  21. ADSP successfully tolerates type-2 attacks Filter + WGC Filter + EGC Tolerates both type-1 and type-2 attacks

  22. How to set the filter threshold? Detection False-alarm Tradeoff between false-alarm vs. detection rate

  23. Finding an optimal filter threshold Optimal filter threshold • Underfiltering • Overfiltering • • number of attackers • • attack strengths Optimal threshold exists in terms of # of valid sensors

  24. Summary & Future work • Attack-tolerant distributed sensing • Develops cluster-based cooperative sensing • Exploits shadow fading correlation • Proposes new date fusion • Future work • Optimal detection/attack strategy • Design of sensing scheduling

  25. Thank you Alexander W. Min alexmin@eecs.umich.edu Visit Real-Time Computing Lab (RTCL) http://kabru.eecs.umich.edu

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